docs: def best_archiver(random, population, archive, args):
      """Archive only the best individual(s).
      
      This function archives the best solutions and removes inferior ones.
      If the comparison operators have been overloaded to define Pareto
      preference (as in the ``Pareto`` class), then this archiver will form 
      a Pareto archive.
      
      .. Arguments:
         random -- the random number generator object
         population -- the population of individuals
         archive -- the current archive of individuals
         args -- a dictionary of keyword arguments
      
      """
      new_archive = archive
      for ind in population:
          if len(new_archive) == 0:
              new_archive.append(ind)
          else:
              should_remove = []
              should_add = True
              for a in new_archive:
                  if ind.candidate == a.candidate:
                      should_add = False
                      break
                  elif ind < a:
                      should_add = False
                  elif ind > a:
                      should_remove.append(a)
              for r in should_remove:
                  new_archive.remove(r)
              if should_add:
                  new_archive.append(ind)
      return new_archive